Predicting springtime herbicide exposure across multiple scales in pacific coastal drainages (Oregon, USA)

Abstract:
Identification of non-point sources of watershed pollution such as pesticide runoff is challenging due to spatial and temporal variation in landscape patterns of land use and environmental conditions. Regional case study monitoring investigations can document region-specific conditions and processes to inform managers about pesticide movement through watersheds. Additionally, modeling field-collected data within these contexts can be used to predict pesticide presence in un-sampled areas. During a 45-day period in the spring of 2019, we sampled sixteen coastal watersheds in Oregon, USA for current-use water-borne herbicides commonly used in forestland vegetation management. At 80 % of sampling locations, at least one of four commonly used herbicides was detected in integrative passive water samplers, with hexazinone and atrazine most commonly detected. In this study, we use total accumulation of detected compounds to compare relative detections with upstream management and environmental watershed variables using multiple linear regression. An additive effects model was developed using slope, herbicide activity notified during the sampling window, and recent clearcut harvest notifications to predict variation in total herbicide accumulation (R2 = 0.8914). The model was then applied to predict concentrations in un-sampled watersheds throughout the Oregon’s coastal region at three watershed scales using Hydrologic Unit Codes (HUCs) 8, 10, and 12. Regional differences in predicted values were visualized using choropleth maps. Subwatersheds (HUC12) were grouped by subbasin (HUC8) and base mean predicted values were compared to further quantify regional differences. Models predicted that south coast sites have higher than average herbicide concentrations, which aligned with field-collected data findings.

Authors: 
Scully-Engelmeyer, Kaegan M.; Granek, Elise F.
Product Number: 
ORESU-R-22-007
Source (Journal Article): 
Ecologicial Indicators, Vol. 142, Sept. 22, 109195
DOI Number (Journal Article): 
10.1016/j.ecolind.2022.109195
Year of Publication: 
2022
Length: 
11 pages